Update app.py

app.py

@@ -3,27 +3,27 @@ import os
 
 import gradio as gr
 import langchain
-import weaviate
+import pickle
 from langchain.vectorstores import Weaviate
+from langchain import OpenAI
 
 from chain import get_new_chain1
 
-WEAVIATE_URL = os.environ["WEAVIATE_URL"]
-
-
-def get_weaviate_store():
-    client = weaviate.Client(
-        url=WEAVIATE_URL,
-        additional_headers={"X-OpenAI-Api-Key": os.environ["OPENAI_API_KEY"]},
-    )
-    return Weaviate(client, "Paragraph", "content", attributes=["source"])
+def get_faiss_store():
+    with open("docs.pkl", 'rb') as f:
+        faiss_store = pickle.load(f)
+        return faiss_store
 
 
 def set_openai_api_key(api_key, agent):
     if api_key:
         os.environ["OPENAI_API_KEY"] = api_key
-        vectorstore = get_weaviate_store()
-        qa_chain = get_new_chain1(vectorstore)
+        vectorstore = get_faiss_store()
+
+        rephraser_llm = OpenAI(model_name="text-davinci-003", temperature=0)
+        final_output_llm = OpenAI(model_name="text-davinci-003", temperature=0, max_tokens=-1)
+
+        qa_chain = get_new_chain1(vectorstore, rephraser_llm, final_output_llm)
         os.environ["OPENAI_API_KEY"] = ""
         return qa_chain
 
@@ -47,7 +47,7 @@ block = gr.Blocks(css=".gradio-container {background-color: lightgray}")
 
 with block:
     with gr.Row():
-        gr.Markdown("<h3><center>LangChain AI</center></h3>")
+        gr.Markdown("<h3><center>Hugging Face Doc Search</center></h3>")
 
         openai_api_key_textbox = gr.Textbox(
             placeholder="Paste your OpenAI API key (sk-...)",
@@ -68,20 +68,20 @@ with block:
 
     gr.Examples(
         examples=[
-            "What are agents?",
-            "How do I summarize a long document?",
-            "What types of memory exist?",
+            "How do I install transformers?",
+            "How do I load pretrained instances with an AutoClass?",
+            "How do I fine-tune a pretrained model?",
         ],
         inputs=message,
     )
 
     gr.HTML(
         """
-    This simple application is an implementation of ChatGPT but over an external dataset (in this case, the LangChain documentation)."""
+    This simple application uses Langchain, an LLM, and FAISS to do Q&A over the Hugging Face Documentation."""
     )
 
     gr.HTML(
-        "<center>Powered by <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></center>"
+        "<center>Powered by <a href='huggingface.co'>Hugging Face 🤗</a> and <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></center>"
     )
 
     state = gr.State()